Duplicate Entries Research Articles

CODEX Meets RACOON - A Concept for Collaborative Documentation of Clinical and Radiological COVID-19 Data.

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the "Electronic Data Capture" (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

Provably curb man-in-the-middle attack-based ARP spoofing in a local network

Even today, internet users’ data security remains a significant concern. One problem is ARP poisoning, otherwise referred to as ARP spoofing. Such attacks are intended to exploit the identified ARP protocol vulnerability. Despite no straightforward remedy for ARP spoofing being apparent, certain actions may be taken to maintain one’s safety. The most basic and common defence against a poisoning attack is manually adding MAC and IP addresses to the static ARP cache table. However, this solution is ineffective for large networks where static entries require considerable time and effort to maintain, whether by human input or via special tools and settings for the static entries of network devices. Accordingly, this paper aimed to monitor network packet information and detect the behaviour of ARP poison attacks on operating systems, for instance Windows and Linux. The discovery and defence policy systematically and periodically check the MAC addresses in the ARP table, enabling alerts to be issued if a duplicate entry is detected. This enables the poison-IP address to be blocked before a reply is sent. Finally, the results showed that the superiority was successfully achieved in the detection, prevention and reporting mechanisms in the real-world environment.

A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease.

Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018–2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community’s growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.

P15 Audit of outcome of ambulatory care emergency surgery patients

Abstract Introduction A proportion of emergency surgical patients are discharged with outstanding tasks (clinical review, imaging to be requested or results followed-up) pending. The turnaround time of these tasks typically placed at the bottom of the emergency surgical on-call list is dependent on the on-call registrar or house officer. The aim was to analyse the outcomes of pending tasks on ambulatory care emergency surgical patients and explore means to improve. Methods A retrospective audit of emergency surgical patients from October to December 2021 was performed using the daily on-call list database. Results Among the 149 patients with pending tasks on an ambulatory care basis, 80 were excluded (duplicate entries, unclear tasks, non-valid reasons). 38% of patients remained on the list for more than 24 hours with pending tasks. Only 75% of patients had pending tasks completed. Patients waited for 3.3 hours on average (59 to 350 minutes) for clinical review by the registrar or house-officer. The reasons for the delay in clinical review and task completion narrowed down to inappropriate timing (scheduled return in the mornings, when the on-call team are either busy with the ward round or in theatres) and time restraints. Conclusion Based on the above findings, it would be beneficial for our hospital to have a surgical hot clinic with staggered appointments rather than ad hoc return to the ambulatory unit. This would improve waiting times and would allow outstanding tasks to be completed by the surgical registrar or clinical fellow during clinic, increasing patient satisfaction. Take-home message A dedicated surgical hot clinic will reduce patient waiting times, increase overall patient satisfaction and ensure completion of outstanding clinical tasks.

Identifying and Reconciling Patients' Allergy Information Within the Electronic Health Record.

Allergy information is often documented in diverse sections of the electronic health record (EHR). Systematically reconciling allergy information across the EHR is critical to improve the accuracy and completeness of patients' allergy lists and ensure patient safety. In this retrospective cohort study, we examined the prevalence of incompleteness, inaccuracy, and redundancy of allergy information for patients with a clinical encounter at any Mass General Brigham facility between January 1, 2018 and December 31, 2018. We identified 4 key places in the EHR containing reconcilable allergy information: 1) allergy modules (including free text comments and duplicate allergen entries), 2) medication laboratory tests results, 3) oral medication allergy challenge tests, and 4) medication orders that have been discontinued due to adverse drug reactions (ADRs). Within our cohort, 718,315 (45.2% of the total 1,588,979) patients had an active allergy entry; of which, 266,275 (37.1%) patient's records indicated a need for reconciliation.

Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study

BackgroundData that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions.ObjectiveWe describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application.MethodsThe University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes.ResultsData collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022.ConclusionsWe provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data.Trial RegistrationClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574International Registered Report Identifier (IRRID)DERR1-10.2196/38294

PO-707-01 MINIATURIZED IMPLANTABLE CARDIAC MONITOR WITH A LONG SENSING VECTOR (BIOMONITOR III): INSIGHTS FROM MAUDE DATABASE

Implantable Cardiac Monitors (ICMs) are used to detect bradycardia, asystole, atrial fibrillation, or other heart rhythm disturbances. BIOMONITOR III (Biotronik, Berlin, Germany) is new generation ICM combining a long sensing vector in a miniaturized profile. We queried the FDA Manufacturer And User Facility Device Experience (MAUDE) database to assess complications types with BIOMONITOR III. The MAUDE database is a repository of industry and patient/physician-reported medical device adverse (MDR) events representing a collection of both common and uncommon events. A MAUDE database search was conducted on November 23, 2021, for reports received between August 1, 2019 to October 31, 2021 to capture all adverse events. After removing duplicate entries, a total of 580 events were reported in 321 MDRs. 34.5% (n=200) were insertion-site events, while 16.4% (n=95) were malfunction related events. The most frequent device malfunction events were: failure to interrogate [24.2%, n=23 post-cardioversion and 1% (n=1) post MRI], sensing issues (20%, n=19), transmission failure (14.7%, n=14) and poor/loss of signals (12.6%, n=12). Of insertion-site events, device externalization (33%, n=66) followed by spontaneous device fall off (23.5%, n=47) and device infection (22%, n=44) were most common. Overall, 43.6% (n=253) underwent device explantation. Several concerns have been reported inherent to the device function (especially device failure post cardioversion), making this a unique reported event. Robust, transparent monitoring mechanisms are warranted especially with newer technologies

OA19 Successes and challenges in harmonising four national juvenile idiopathic arthritis cohorts: an example from CLUSTER consortium

Abstract Background/Aims The CLUSTER consortium aims to identify biomarkers and strata that improve personalised treatments for JIA/JIA-uveitis. By bringing together knowledge and data, CLUSTER can conduct novel analyses in this rare, heterogeneous disease. Data harmonisation across existing JIA cohorts facilitates new, larger datasets that would otherwise take years to collect; however, challenges exist as datasets are often collected autonomously. Here we present progress towards a large-scale, unique JIA data resource, bringing together treatment data from four real-world JIA treatment studies. Methods Four studies (CAPS, CHARMS, BCRD and BSPAR-ETN; the latter two being part of the UK JIA Biologics register) contributed data into CLUSTER. We created two clinical datasets of JIA patients starting first-line methotrexate (MTX) or tumour necrosis factor inhibitors (TNFi). Variables were selected based on a previously developed core dataset, accounting for different levels of granularity across studies. The same inclusion and exclusion criteria were agreed for both datasets, designed to allow for combined analysis of these. OpenPseudonymiser software encrypted NHS numbers - these were matched cross-study to identify duplicates and checked against known duplicate lists. Errors in NHS numbers and existing duplicate matches were identified and corrected. Each NHS number was assigned a CLUSTER ID, meaning one child has the same ID across all relevant studies such that children contributing similar data across multiple studies could be identified. Results A total of 7013 records (from 5435 individuals) were identified, of which 2882 (41%, corresponding to 1304 individuals) represented the same child across &amp;gt;1 study. 197 individuals had duplicate records within one study, 961 in two studies, 142 in three, and four children had duplicate records in all four studies. After removing 350 MTX and 605 TNFi duplicate entries, the final datasets contain 2899 and 2401 unique MTX and TNFi patients respectively; 1018 are in both datasets having received both treatments. Missingness across core outcome variables ranged from 10% (active joint count MTX timepoint 2) to 60% (physician VAS TNFi timepoint 2) and was not improved through combining datasets with duplicate entries. Specificity in some variables was lost to allow integration by combining data using least common denominators (e.g. ethnicity captured as Caucasian/Non-Caucasian, despite more specific categories available in some studies). Conclusion Combining data across studies has achieved dataset sizes rarely seen in JIA, which is invaluable to progressing research into personalised treatments and disease outcomes. However, losing specificity in some variables and missingness (a known challenge in observational data) and their impact on future analyses requires further consideration. Ongoing work includes identifying patients with both clinical and biological data that can be combined for more in-depth analyses. Both datasets are available for researchers to use via the CLUSTER Consortium Data Management Committee. Disclosure S. Lawson-Tovey: None. L.R. Wedderburn: Consultancies; L.W. reports consulting fees from Pfizer unrelated to this work. Grants/research support; CLUSTER consortium receives support from AbbVie, UCB, Pfizer, Sobi and GSK. N. Geifman: None. M. Barnes: None. K.L. Hyrich: Grants/research support; KLH reports grant income from BMS, UCB, and Pfizer. Other; KLH reports non-personal speaker's fees from Abbvie.

A Machine Learning-Based Method for Content Verification in the E-Commerce Domain

Analysis of extreme-scale data is an emerging research topic; the explosion in available data raises the need for suitable content verification methods and tools to decrease the analysis and processing time of various applications. Personal data, for example, are a very valuable source of information for several purposes of analysis, such as marketing, billing and forensics. However, the extraction of such data (referred to as person instances in this study) is often faced with duplicate or similar entries about persons that are not easily detectable by the end users. In this light, the authors of this study present a machine learning- and deep learning-based approach in order to mitigate the problem of duplicate person instances. The main concept of this approach is to gather different types of information referring to persons, compare different person instances and predict whether they are similar or not. Using the Jaro algorithm for person attribute similarity calculation and by cross-examining the information available for person instances, recommendations can be provided to users regarding the similarity or not between two person instances. The degree of importance of each attribute was also examined, in order to gain a better insight with respect to the declared features that play a more important role.

Characterizing Complications of Intracranial Responsive Neurostimulation Devices for Epilepsy Through a Retrospective Analysis of the Federal MAUDE Database

ObjectivesResponsive neurostimulation is an innovative modality in the treatment of medication-refractory epilepsy for patients who are not suitable candidates for surgical intervention. While being a potentially life-changing treatment option for many individuals with epilepsy, little is known about the system's complications aside from its performance in initial clinical trials. Therefore, the goal of this study was to characterize all reported complications of the RNS system made to the Food & Drug Administration since its approval. Materials and MethodsThe Manufacturer and User Facility Device Experience (MAUDE) database was queried for entries reported under “implanted brain stimulator for epilepsy” through the dates of November 1, 2013, to March 1, 2020. After correction of duplicate entries, each was sorted into complication types based on the entries' narrative descriptions. ResultsThe searched yielded 241 unique complication events. The most common complications were attributed to infections (40%) and lead breaks (12%). Other reported complications included poor wound healing (10%) and intrinsic device failure (7%). Focal neurological deficits were found in 2%. Over half (67%) of the reported complications required return to the operating room for revision or explant. The remainder of the adverse events were self-resolved or treated with either medication or software adjustment. ConclusionsFuture research endeavors should attempt to optimize the implantable device for preventing infections. The data of complications provided by this review will also aid physicians in providing the most accurate informed consent for patients when deciding to undergo implantation with the responsive neurostimulation system.

Semantic interoperability in health records standards: a systematic literature review.

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

Knowledge and Perceptions of Reactive Arthritis Diagnosis and Management Among Healthcare Workers During the COVID-19 Pandemic: Online Survey.

Reactive arthritis (ReA) is an often neglected disease that received some attention during the coronavirus disease 2019 (COVID-19) pandemic. There is some evidence that infection with severe acute respiratory syndrome coronavirus 2 can lead to "reactive" arthritis. However, this does not follow the classical definition of ReA that limits the organisms leading to this condition. Also, there is no recommendation by any international society on the management of ReA during the current pandemic. Thus, a survey was conducted to gather information about how modern clinicians across the world approach ReA. An e-survey was carried out based on convenient sampling via social media platforms. Twenty questions were validated on the pathogenesis, clinical presentation, and management of ReA. These also included information on post-COVID-19 arthritis. Duplicate entries were prevented and standard guidelines were followed for reporting internet-based surveys. There were 193 respondents from 24 countries. Around one-fifth knew the classical definition of ReA. Nearly half considered the triad of conjunctivitis, urethritis and asymmetric oligoarthritis a "must" for diagnosis of ReA. Other common manifestations reported include enthesitis, dermatitis, dactylitis, uveitis, and oral or genital ulcers. Three-fourths opined that no test was specific for ReA. Drugs for ReA were non-steroidal anti-inflammatory drugs, intra-articular injections, and conventional disease-modifying agents with less than 10% supporting biological use. The survey brought out the gap in existing concepts of ReA. The current definition needs to be updated. There is an unmet need for consensus recommendations for the management of ReA, including the use of biologicals.

Knowledge, Awareness, and Practices (KAP) towards COVID-19 among the marine fishers of Maharashtra State of India: An online cross-sectional Analysis.

The knowledge, awareness, and practices (KAP) about COVID-19 among the marine fishers who are engaged in the high-risk occupations and depend on the vulnerable sources of income in the Maharashtra state of India, were investigated to understand the behavioral changes and vulnerability among the respondents because of COVID-19. Data were collected through an online survey from 1st July to 30th July 2020 during the first peak of COVID-19 in the study area by using the questionnaire. Authors based on the latest recommendations by the World Health Organization (WHO) developed the questionnaire and covered the KAP aspects under study. The questionnaire was administered through Google form link and the link was disseminated through social media such as Facebook and What’s App. The completed responses (n = 533) received from the eligible sample, excluding those outsides of the fisher community and duplicate entries were considered for analysis. Appropriate statistical tools were used to analyze the data. Among the respondents, 447 men (83.86%) and 86 women (16.14%), with a mean age of 45.4 years (M = 45.4, SD = 1.18). The results revealed that 70.92% of respondents perceived a low risk of infection because of COVID-19 with M = 1.77 and SD = 0.583, but authors found the perceived severity score high (M = 3.95, SD = 1.21) among the 60.97% of respondents. Around 95% of respondents were aware of the COVID-19, while almost all respondents knew the medium of infection and symptoms of COVID-19. The behavior of respondents towards COVID-19 infection was positive and was following sufficient precautionary measures suggested by WHO to protect themselves from COVID-19 infection. The results of multiple regression analysis revealed that the demographic features of respondents like age, education were significantly contributing to the KAP of the fisher community. The results confirmed the importance of KAP which is reflected in the behavioral responses of the fishers while tackling the COVID-19 pandemic situation. The results regarding the risk perception, severity, KAP about COVID-19 provide a collective understanding of the emerging infectious disease among the marine fishers of Maharashtra state of India.

Analysis of the factors influencing the elimination strategies with the current status of diagnosis and treatment of hepatitis C in hospital

Objective: To understand the current status of screening, diagnosis, and treatment and analyze the factors influencing micro-elimination strategy, so as to achieve hepatitis C elimination in hospital. Methods: Anti-HCV and HCV RNA test results of patients from October 2017 to September 2020 were retrospectively analyzed. Anti-HCV positive rates and factors influencing different genders, ages, places of residence and departments were analyzed. After comparing anti-HCV-positive patients with HCV RNA-positive patients with duplicate entries in "Name" and "Date of birth", the data were divided into three categories: anti-HCV positive without HCV RNA test, HCV RNA positive in single test, and HCV RNA positive many times in multiple tests. The above three types of patients were followed-up by telephone. According to the hospital follow-up results, current status of diagnosis and treatment and the factors influencing the micro-elimination strategy of hepatitis C were studied and analyzed. The comparison of data between groups were performed using χ(2) or χ(2) continuity-correction test. Results: Anti-HCV positive detection rate was 1.34% (899/66 866). The positive rate of male patients aged 40 and over residing in cities was significantly higher than female patients under 40 years old residing in rural areas, and the difference was statistically significant (χ(2) = 55.178, 264.11, 36, 351, P < 0.05). There were 90 (10.02%) and 809 cases (89.98%) in outpatient and inpatient departments, respectively, with no statistically significant difference between the two (χ(2) = 0.002, P > 0.05). The total number of anti-HCV positive cases were 196 in Gastroenterology (22.0%), 75 in Respiratory and Critical Care Medicine (8.3%), 74 in Neurology (8.2%), 63 in Orthopedics (7.0%) and 55 in Endocrinology departments (6.1%), and the difference in the positive rate among different departments were also statistically significant (χ(2) = 271.585, P < 0.05). Among the 480 cases who were followed-up, 215 (44.79%) were lost to follow-up, 84 cases (39.07%) were unregistered, 77 cases (16.04%) were untreated, 15 cases (19.48%) were unaware of their state of illness, 46 cases (59.74%) were diagnosed without concern, 16 cases (20.78%) were diagnosed but did not take medicine, 60 cases were under treatment, and 29 cases were mostly on counterfeit drugs (48.33%). Conclusion: Comprehensive diagnosis and treatment education to non-specialist clinicians and timely manner regular follow-up of patients is a key factor and an important link to formulate a simple, easy and sustainable model to improve the efficiency of screening, diagnosis, and treatment of hepatitis C micro-elimination strategy in hospital. In addition, it will also play an important role in achieving the strategic goal of "eliminating hepatitis C as a public health threat by 2030".

Abstract 14439: Complications With Medtronic’s SelectSecure 3830 Leads When Used for Atrial and Ventricular Pacing versus Conduction System Pacing in the United States

Introduction: Conduction system pacing is physiological and is therefore gaining interest. The Medtronic SelectSecure 3830 lead was officially approved by the FDA for His-bundle pacing (HBP) in 2018. However, prior to 2017-2018, this lead was mainly used for atrial pacing, ventricular pacing, or both. We evaluated and compared nationwide adverse events and complications associated with this lead prior to and after 2017. Hypothesis: Compare adverse events and complications associated with Medtronic’s SelectSecure 3830 leads before 2017 when it was mainly used for atrial, ventricular, or atrial and ventricular pacing to events after 2017 when it was mainly used for HBP. Methods: We collected adverse event reports from the FDA’s Manufacturer and User Facility Device Experience (MAUDE) on SelectSecure 3830 leads between 2008 and 2020. We searched the data base with key words “selectsecure” and “select secure” along with the model number “3830” to aggregate the data. Duplicate entries were screened and removed, and the location the leads were used was classified. Results: Total number of adverse events reported before 2017 was 393 (90.5%) when compared to after 2017, 41 (9.5%). The percentage of adverse events reported when the lead was used for atrial, ventricular, or atrial and ventricular pacing was 95.3%, when compared to 4.7% when the lead was used for HBP. The most common adverse events reported were capture and threshold issues (16.8%; PRR for atrial/ventricular lead location 0.76 [95% CI: 0.32-1.78] vs. 1.30 [95% CI: 0.55-3.06] for His-bundle lead location) and lead dislodgement (15.6%; PRR for atrial/ventricular lead location 1.03 [95% CI: 0.27-3.81] vs. 0.96 [95% CI: 0.26-3.58] for His-bundle lead location); which were comparable. Conclusion: Medtronic’s SelectSecure 3830 leads had comparable capture/threshold and lead dislodgement issues regardless of the atrial, ventricular, or His-bundle location.

Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model.

Introduction Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10′N, longitude 102°18′E) was analysed with dengue data. Result A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.

Pseudonatural Products Occur Frequently in Biologically Relevant Compounds.

A new methodology for classifying fragment combinations and characterizing pseudonatural products (PNPs) is described. The source code is based on open-source tools and is organized as a Python package. Tasks can be executed individually or within the context of scalable, robust workflows. First, structures are standardized and duplicate entries are filtered out. Then, molecules are probed for the presence of predefined fragments. For molecules with more than one match, fragment combinations are classified. The algorithm considers the pairwise relative position of fragments within the molecule (fused atoms, linkers, intermediary rings), resulting in 18 different possible fragment combination categories. Finally, all combinations for a given molecule are assembled into a fragment combination graph, with fragments as nodes and combination types as edges. This workflow was applied to characterize PNPs in the ChEMBL database via comparison of fragment combination graphs with natural product (NP) references, represented by the Dictionary of Natural Products. The Murcko fragments extracted from 2000 structures previously described were used to define NP fragments. The results indicate that ca. 23% of the biologically relevant compounds listed in ChEMBL comply to the PNP definition and that, therefore, PNPs occur frequently among known biologically relevant small molecules. The majority (>95%) of PNPs contain two to four fragments, mainly (>95%) distributed in five different combination types. These findings may provide guidance for the design of new PNPs.

Methods for Authenticating Participants in Fully Web-Based Mobile App Trials from the iReach Project: Cross-sectional Study

BackgroundMobile health apps are important interventions that increase the scale and reach of prevention services, including HIV testing and prevention counseling, pre-exposure prophylaxis, condom distribution, and education, of which all are required to decrease HIV incidence rates. The use of these web-based apps as well as fully web-based intervention trials can be challenged by the need to remove fraudulent or duplicate entries and authenticate unique trial participants before randomization to protect the integrity of the sample and trial results. It is critical to ensure that the data collected through this modality are valid and reliable.ObjectiveThe aim of this study is to discuss the electronic and manual authentication strategies for the iReach randomized controlled trial that were used to monitor and prevent fraudulent enrollment.MethodsiReach is a randomized controlled trial that focused on same-sex attracted, cisgender males (people assigned male at birth who identify as men) aged 13-18 years in the United States and on enrolling people of color and those in rural communities. The data were evaluated by identifying possible duplications in enrollment, identifying potentially fraudulent or ineligible participants through inconsistencies in the data collected at screening and survey data, and reviewing baseline completion times to avoid enrolling bots and those who did not complete the baseline questionnaire. Electronic systems flagged questionable enrollment. Additional manual reviews included the verification of age, IP addresses, email addresses, social media accounts, and completion times for surveys.ResultsThe electronic and manual strategies, including the integration of social media profiles, resulted in the identification and prevention of 624 cases of potential fraudulent, duplicative, or ineligible enrollment. A total of 79% (493/624) of the potentially fraudulent or ineligible cases were identified through electronic strategies, thereby reducing the burden of manual authentication for most cases. A case study with a scenario, resolution, and authentication strategy response was included.ConclusionsAs web-based trials are becoming more common, methods for handling suspicious enrollments that compromise data quality have become increasingly important for inclusion in protocols.International Registered Report Identifier (IRRID)RR2-10.2196/10174

Alliance Rules- based Algorithm on Detecting Duplicate Entry Email

The way that email has extraordinary significance in present day business communication is certain. Consistently, a bulk of emails is sent from organizations to clients and suppliers, from representatives to their managers and starting with one colleague then onto the next. In this way there is vast of email in data warehouse. Data cleaning is an activity performed on the data sets of data warehouse to upgrade and keep up the quality and consistency of the data. This paper underlines the issues related with dirty data, detection of duplicatein email column. The paper identifies the strategy of data cleaning from adifferent point of view. It provides an algorithm to the discovery of error and duplicates entries in the data sets of existing data warehouse. The paper characterizes the alliance rules based on the concept of mathematical association rules to determine the duplicate entries in email column in data sets.

Pattern of congenital heart diseases among children attending a pediatric cardiology OPD of a tertiary level hospital in West Bengal, India: a retrospective study

Background: Congenital heart disease (CHD) is one of the most common congenital defects worldwide. The prevalence of CHD is low in India mostly due to under detection. The knowledge regarding socio-demographic factors, other congenital anomalies/syndromes and social impacts associated with CHDs in India is grossly lacking.Methods: This was a retrospective study. Details of all diagnosed pediatric (age 0-14 years) cases of CHD were recorded by a single cardiologist from the year 2013 to 2020. Duplicate entries were removed and CHDs were classified according to Q20-Q28 of tenth revision of international classification of diseases (ICD). For description purposes, the CHDs were divided into 10 sub-groups.Results: Total 9247 cases of CHDs were recorded during the study period. Ventricular septal defect was the most commonly identified CHD (32.1%). Most cases were acyanotic (69.8%) and presented first time at age of more than two years. Almost 1.5% cases were associated with other non-cardiac congenital defect or syndrome. In 1.0% of cases either mother or the child faced social or family problem due to the disease.Conclusions: CHD is an emerging public health problem. It is associated with other congenital anomalies/syndrome. Mandatory screening for birth defects needs to be initiated at all birth facilities. At birth counseling of the parents need to be implemented due to the associated social issues.